MOFAcell documentation

MOFAcell.md

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+---
+layout: default
+title: Multicellular Factor Analysis - Repurposing MOFA for multicellular integration
+---
+
+Cross-condition single-cell omics data profile the variability of cells across cell-types, patients, and conditions. Multicellular factor analysis (MOFAcell) repurposes MOFA to estimate cross-condition multicellular programs from single-cell data. These multicellular programs represent coordinated molecular changes occurring in multiple cells and can be used for the unsupervised analysis of samples in single-cell data of multiple samples and conditions. The flexibility in view creation allows the inclusion of structural (eg. spatial dependencies) or communication tissue-level views in the inference of multicellular programs. Leveraging on MOFA’s structured regularization MOFAcell is also suitable for meta-analysis and the joint modeling of independent studies.
+
+<p align="center"> 
+<img src="images/MOFAcellFig.png" width="60%"/>​
+</p>
+
+For more details you can read our paper: \n
+
+-  [*Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease (eLife 2023)*](https://elifesciences.org/articles/93161)
+
+## Use
+
+We have created a complementary R package [MOFAcellulaR](https://github.com/saezlab/MOFAcellulaR) that contains helper fuctions to prepare your single-cell data for a multicellular factor analysis with MOFA.
+
+A python implementation with [muon](https://muon.scverse.org/) is available through [liana-py](https://liana-py.readthedocs.io/en/latest/index.html)
+
+## Tutorials/Vignettes
+* [**Running a multicellular factor analysis in a cross-condition single-cell atlas**](https://saezlab.github.io/MOFAcellulaR/articles/get-started.html): illustration of the method with a toy example
+
+### Python Tutorials
+ * [**Running a multicellular factor analysis in a cross-condition single-cell atlas**](https://liana-py.readthedocs.io/en/latest/notebooks/mofacellular.html): illustration of the method with real data
+ * [**Multicellular factor analysis for intercellular context factorization**](https://liana-py.readthedocs.io/en/latest/notebooks/mofatalk.html): inference of multicellular programs from cell-cell communication inference scores

index.md

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 - general framework: [published in Molecular Systems Biology](http://msb.embopress.org/cgi/doi/10.15252/msb.20178124)  
 - multi-group framework and single cell applications: [MOFA+, published in Genome Biology](http://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-02015-1)
 - temporal or spatial data: [MEFISTO, published in Nature Methods](https://www.nature.com/articles/s41592-021-01343-9)
+- multicellular factor analysis [MOFAcell, published in eLife](https://elifesciences.org/articles/93161)
 
 ## Implementation